In MATLAB, scalars are 1x1 arrays, and thus they can be indexed. There have been situations in my use of Numpy when I would have liked to index scalars to make my code more general.
It's not a very pressing issue for me but it is an interesting issue. Whenever I index an array with a sequence or slice I'm guaranteed to get another array out. This consistency is nice. In [1]: A=numpy.random.rand(10) In [2]: A[range(0,1)] Out[2]: array([ 0.88109759]) In [3]: A[slice(0,1)] Out[3]: array([ 0.88109759]) In [3]: A[[0]] Out[3]: array([ 0.88109759]) However, when I index an array with an integer, I can get either a sequence or a scalar out. In [4]: c1=A[0] Out[4]: 0.88109759 In [5]: B=numpy.random.rand(5,5) In [5]: c2=B[0] Out[5]: array([ 0.81589633, 0.9762584 , 0.72666631, 0.12700816, 0.40653243]) Although c1 and c2 were derived by integer-indexing two different arrays of doubles, one is a sequence and the other is a scalar. This lack of consistency might be confusing to some people, and I'd imagine it occasionally results in programming errors. Damian Travis E. Oliphant wrote: > Hi everybody, > > In writing some generic code, I've encountered situations where it would > reduce code complexity to allow NumPy scalars to be "indexed" in the > same number of limited ways, that 0-d arrays support. > > For example, 0-d arrays can be indexed with > > * Boolean masks > * Ellipses x[...] and x[..., newaxis] > * Empty tuple x[()] > > I think that numpy scalars should also be indexable in these particular > cases as well (read-only of course, i.e. no setting of the value would > be possible). > > This is an easy change to implement, and I don't think it would cause > any backward compatibility issues. > > Any opinions from the list? > > > Best regards, > > -Travis O. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
